1. Field of the Invention
The invention relates to an image processing method, and more particularly to an image processing method for magnetic resonance imaging.
2. Description of the Related Art
Diffusion MRI is a non-invasive imaging method suitable for evaluating fiber orientation of a specific region and revealing the underlying white matter structure of the human brain. One of the approaches is the diffusion tensor imaging (DTI), where the water diffusion is modelled as Gaussian distribution. Practically, a DTI dataset consists of six diffusion weighted (DW) images by applying the diffusion-sensitive gradients in six non-colinear directions, and one null image with no application of diffusion-sensitive gradient. The water diffusion is encoded in the signal intensity of the DW images. Therefore, with appropriate processing on the signal intensity of the DTI dataset, the diffusion tensor in each voxel can be estimated. The diffusion tensor could be represented by a symmetric 3-by-3 matrix, where the principal eigenvector of the matrix is usually assumed to coincide with the underlying fiber orientation. The fiber orientation map can be further processed to reconstruct the fiber pathways.
However, the Gaussian assumption limits DTI to detect at most one fiber orientation in each voxel. Consequently, in regions with crossing fibers, it is difficult for DTI to resolve the fiber orientations and would lead to inaccurate estimation of the anisotropy index.
The crossing fiber problem could be resolved through estimating the diffusion orientation distribution function (ODF). The diffusion ODF could be estimated by the high angular resolution diffusion image (HARDI) methods, such as q-ball imaging (QBI), or by a grid sampling scheme, which is also called diffusion spectrum imaging (DSI). All of the methods do not impose any diffusion models.
In neuroimage studies, it is usually required to transform the brain images to a common space, the so-called template space, to perform the analyses (e.g., statistical comparisons between healthy and patient groups). It is known in the art to use a linear or non-linear method to transform a three-dimensional (3D) brain image to the template space. However, the conventional 3D transformation methods can only deal with scalar images. For an appropriate transformation on diffusion images such as DTI, QBI or DSI, not only the anatomical structures need to be registered, but the diffusion profiles require to be aligned.